Computation of order and volume fill rates for a base stock inventory control system with heterogeneous demand to investigate which customer class gets the best service

We consider a base stock inventory control system serving two customer classes whose demands are generated by two independent compound renewal processes. We show how to derive order and volume fill rates of each class. Based on assumptions about first order stochastic dominance we prove when one customer class will get the best service. That theoretical result is validated through a series of numerical experiments which also reveal that it is quite robust.Base stock policy; service measures; two customer classes; compound renewal processes

Variety Management in Assemble-to-Order Supply Chains

Assemble-to-order refers to a supply chain strategy in which products are not assembled until customer order arrives. It is based on the so-called form postponement that is to hold components at a generic form and to delay the point of product differentiation. The performance of an assem-ble-to-order supply chain depends on two main dimensions, which are responsiveness and achievement level of scale economies. Responsiveness refers to the capability of fulfilling customer requirements in a fast-paced manner, whereas the achievement of scale economies reflects the degree of operations efficiency. Assemble-to-order supply chains induce high product variety, which has adverse effects on performance. We use demand volumes as a proxy for scale economies and lead times as a proxy for responsiveness. A matrix that consists of both dimensions can be defined, in which we distinguish between short/long lead times and low/high demand volumes. This matrix is called performance matrix. On the other hand, the consequence that results from product variety is a high demand variability of end products, which also affects the demand variability of components. An analysis of component demand variability enables one to identify the components with low/high demand variability. These components can further be classified into supplied and in-house made components. Thus, a second matrix (called component matrix) with two dimensions, namely variability (low/high) and supply source (in-house/supplier) can be defined. Due to the supply source dimension in the component matrix, the supply chain perspective is also taken into ac-count. The combination of both matrixes into a single one provides the performance/component matrix for assemble-to-order supply chains. To use the final matrix, it is necessary to compute lead times, demand volumes and demand variability of the supplied and in-house made components. By plotting the components in the matrix, one can determine the problems induced by variety. In order to improve the performance of the assemble-to-order supply chain, the implementation of variety management strategies is necessary. The identified strategies are: commonality, component families, modularity, and platforms. Based on the performance/component matrix, we discuss how these strategies or a combination of them can contribute to derive recommendations that aim to alleviate variety impacts on the as-semble-to-order supply chain.Assemble-to-order; Supply Chain Management; Variety Management

Optimal postponement in supply chain network design under uncertainty: an application for additive manufacturing

This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterized by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology for meeting each market’s demand, each operation’s optimal production quantity, and each selected technology’s optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies.This work was developed under an Accenture Open Innovation University [grant number I-01326] and was also partially supported by grant RTI2018-097580-B-I00 of the Ministry of Economy and Competitiveness of Spain.Peer ReviewedPostprint (published version

Practices for strategic capacity management in Malaysian manufacturing firms

While the notion of manufacturing capabilities is a long-standing notion in research on operations management, its actual implementation and management has been hardly researched. Five case studies in Malaysia offered the opportunity to examine the practice of manufacturing managers with regard to strategic capability management. The data collection and analysis was structured by using the notion of Strategic Capacity Management. Whereas traditionally literature has demonstrated the beneficial impact of an appropriate manufacturing strategy on the business strategy and performance, the study highlights the difficulty of managers to set the strategy, let alone implementing it. This is partly caused by the immense pressure of customers in these dominantly Make-To-Order environments for SMEs. Current concepts for manufacturing capabilities have insufficiently accounted this phenomenon and an outline of a research agenda is presented

Exploring risk pooling in hospitals to reduce demand and lead time uncertainty

Nearly every eighth German hospital faces an elevated risk of bankruptcy. An inappropriate use of inventory management practices is among the causes. Hospitals suffer from demand and lead time uncertainty, and the current COVID-19 pandemic worsened the plight. The popular business logistics concept of risk pooling has been shown to reduce these uncertainties in industry and trade, but has been neglected as a variability reduction method in healthcare operations research and practice. Based on a survey with 223 German hospitals, this study explores how ten risk pooling methods can be adapted and applied in the healthcare context to reduce economic losses while maintaining a given service level. The results suggest that in general risk pooling may improve the economic situation of hospitals and, in particular, inventory pooling, transshipments, and product substitution for medications and consumer goods are the most effective methods in the healthcare context, while form postponement may be unsuitable for hospitals due to the required efforts, delay in treatments, and liability issues. The application of risk pooling in healthcare requires willingness to exchange information and to cooperate, adequate IT infrastructure, compatibility, adherence to healthcare laws and regulations, and securing the immediate treatment of emergencies. Compared to manufacturing and trading companies, hospitals seem to currently neglect the variability reducing effect of risk pooling

A Computer-Based Simulation Investigation of Environment-Strategy Fit for Risk Management in Global Supply Chains

The purpose of this dissertation is to examine the phenomenon of risk management in global supply chains. Drawing from logistics, supply chain management, operations management, economics, international business, and strategy literatures and a qualitative study, a comprehensive conceptual model of environment-strategy fit for risk management in global supply chains was developed. External environmental conditions comprising of supply and demand risks, four risk management strategies, namely hedging, assuming, postponement, and speculation, and a moderator in the form of a port disruption were chosen for further investigation. The model was quantitatively tested using a simulation. The findings from this dissertation study reflect mixed results. Findings that conform to existing research, primarily related to hedging and speculation strategies, provide empirical support for extant knowledge that is primarily conceptual or experience-based. On the other hand, findings that are contrary to existing knowledge or are supported under very select conditions, primarily related to assuming and postponement strategies, provide interesting new insights into the phenomenon. The findings add to both theoretical and practical understanding of the phenomenon. This research opens up several new research directions that indicate that continued research is needed to facilitate both theoretical and empirical progress in better understanding of risk management in global supply chains